Jul 2026· Journal of Intelligent Decision Making and Information Science· Vol 3, pp. 787-795· 0 citations· 17 references
TL;DR
It is emphasized that to sustain long-term involvement, software developers must engineer customizable user interfaces and gamified skill-tracking mechanisms that actively support independence, user capability, and digital social connection.
Abstract
Despite the rapid growth of educational technologies, smart campus systems often lack empirical validation regarding user motivation. This study assessed student engagement in extracurricular activities to establish quantitative user requirements for designing AI-integrated management platforms. A descriptive-quantitative research design was employed in a public senior high school involving 89 student respondents. Utilizing a survey method with a modified standardized questionnaire, baseline data was collected on how the psychological needs for autonomy, competence, and relatedness influence participation. The findings reveal that these fundamental psychological needs significantly impact engagement and serve as vital parameters for technology acceptance. Furthermore, students demonstrated minimal engagement in administrative leadership roles, highlighting an operational friction gap that artificial intelligence could effectively automate. The results emphasize that to sustain long-term involvement, software developers must engineer customizable user interfaces and gamified skill-tracking mechanisms that actively support independence, user capability, and digital social connection. Ultimately, educational institutions should deploy these empirically designed management ecosystems to foster user adoption, promote holistic student development, and ensure sustained participation in school organizations.
This study evaluates the feasibility of integrating Gemini AI to counteract declining student motivation driven by conventional teaching methodologies at Pamulang University, South Tangerang. Utilizing an integrated SWOT and TELOS (Technical, Economic, Legal, Operational, and Schedule) framework, the research analyzes empirical survey data from Information Systems students. The findings reveal that 97.6% of respondents belong to Generation Z, a demographic natively receptive to digital tools. Furthermore, 78.6% had independently adopted Gemini AI for academic purposes prior to institutional intervention, while 87.8% agree that diverse digital materials significantly enhance classroom interaction. The SWOT analysis highlights critical internal strengths, including heightened motivation and broader knowledge access, alongside interactive learning environments. Conversely, a severe internal weakness is the university's infrastructure, with 54.8% of students reporting unstable internet connectivity. While high independent adoption presents a strong external opportunity, the primary external threat stems from an overreliance on AI that could compromise critical thinking and analytical problem-solving skills. From a TELOS perspective, implementing Gemini AI is both economically and legally viable, aligning with Indonesian frameworks such as the Ministry of Communication Circular No. 9/2023 and Law No. 27/2022 on Personal Data Protection. Nevertheless, the institution's technical readiness falls below the required threshold and demands substantial enhancement. To ensure a responsible transition to AI-assisted education, this study recommends a phased strategy: upgrading IT infrastructure, providing regular AI literacy training for educators, and establishing structured faculty supervision protocols to mitigate student dependency.
Raihand Ramadhani Abdul Sayeed, Emi Sita Eriana, Afrizal Zein· bit-Tech· 0 citations
This study examined the appropriation of artificial intelligence tools among college teachers using the Model of Technology Appropriation as a framework and viewing the process through a human resource management lens. A total of 165 college teachers from the Philippines, selected through purposive sampling, completed an online questionnaire measuring six constructs across three levels of appropriation: evaluation, adaptation, and incorporation. The results showed that all six constructs fell within the strongly agree range, with the evaluation level scoring highest and the adaptation level scoring lowest; institutional support, in particular, showed the widest variation among teachers. All three levels were strongly and significantly correlated with one another, and among the two adaptation constructs, social shaping showed a stronger relationship with incorporation than institutional support. When grouped by teacher profile, licensure status showed no significant difference in incorporation, with licensed and non-licensed teachers reporting identical median scores. Academic rank showed a significant difference, with associate professors scoring higher than both assistant professors and instructors. Years of teaching experience and age both showed small but statistically significant negative correlations with incorporation. The results indicate that teachers reported favorable evaluation of the usability and value of AI tools, together with lower and more variable ratings of institutional support. Institutional support, social shaping, and differences across academic ranks are therefore areas that merit closer attention. The study offers practical implications for how academic institutions might design more targeted, rather than uniform, support for AI tool integration across their teaching workforces.
Rolaida L. Sonza, Jovita G. Rivera, Amado B. Martinez et al.· Human Resources Management a...· 0 citations
This article examines the role of artificial intelligence (AI) in enhancing human resource management (HRM) within the education industry. Building upon recent growth, it examines how AI-enabled tools automate repetitive HR tasks, support data-driven decision-making, and augment strategic functions such as recruitment, workforce planning, and staff development. A quantitative research design was adopted, with a purposive sample of 50 HR professionals from educational institutions. Data was collected through a structured questionnaire using a five-point Likert scale and analyzed with descriptive statistics. Findings indicate high levels of awareness of AI among HR professionals and strong agreement that AI improves recruitment accuracy, workforce planning and personalized training. Respondents also acknowledged AI’s value in enhancing employee engagement and supporting data-driven HR decision-making, although concerns about data privacy and reduced human interaction remain. The study contributes to ongoing debates on digital transformation in HRM by providing empirical evidence from the education sector and suggests directions for both practice and future research.
Yasmin Mirzani, Anshuman Shastri· International journal of com...· 0 citations
: The research examines how artificial intelligence (AI) is used in business education by studying how educators perceive AI and how it influences their teaching methods and student participation and how it creates both challenges and possibilities for educational institutions. Data collection at Pangasinan State University utilized a mixed-method approach which included surveys and semi-structured interviews and focus group discussions and document analysis. Most educators have moderate AI knowledge according to the findings but they need specific training because of their low confidence in using AI. AI implementation improved teaching methods and student engagement because it used personalized learning and interactive educational tools. The main challenges for institutions stemmed from ethical dilemmas and digital access disparities and their respective institutional readiness levels. Educational institutions need to build professional development programs and educational technology infrastructure and create methods that support all students in order to make AI an educational tool in business studies. Institutions that implement AI technology gain a competitive advantage because it helps them create their educational identity and modernize their institutional appeal.
Josephine Diadid-Reyes· Proceedings of the 1st Inter...· 0 citations
Artificial intelligence (AI) has rapidly permeated higher education workplaces, yet a significant disconnect exists between employee adoption of AI tools and institutional policy awareness, governance structures, and strategic clarity. This study examines the emergent phenomenon of the "AI implementation gap" in higher education—the disparity between widespread AI tool usage and the institutional frameworks meant to guide such use. Drawing on recent survey data from nearly 2,000 higher education professionals and situating findings within broader theoretical frameworks of technology adoption, organizational change, and higher education governance, this article critically analyzes the current state of AI integration in higher education work environments. Key findings reveal that while 94% of higher education employees report using AI tools for work, only 54% are aware of relevant institutional policies, and more than half have used AI tools not sanctioned by their institutions. The analysis explores the risks, opportunities, and challenges associated with this implementation gap, including concerns about data privacy, misinformation, skill erosion, algorithmic bias, environmental impact, and the largely unmeasured return on investment of AI initiatives. The article also examines the roles of AI vendors, the ethical dimensions of AI adoption, and the implications of voluntary versus mandated technology use. The article concludes with recommendations for institutional leaders, policymakers, and researchers seeking to bridge the gap between AI adoption and governance in higher education contexts.
Jonathan H. Westover· Future of Work: The Journal...· 0 citations
The rapid institutionalization of generative artificial intelligence (GenAI) in higher education has created an urgent need for empirical evidence on how structured course-level integration relates to student engagement and perceptions of learning. This study examines a usefulness-centered conceptual framework combining elements of the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology (TAM/UTAUT) with the digital competence perspective of DigComp 2.2. A structured pedagogical pilot intervention requiring all students to use generative AI tools was implemented in an undergraduate Public Service Management course (n = 76). Students completed AI-supported group assignments and an immediate post-intervention questionnaire comprising 19 Likert-scale items, four demographic questions, and four optional open-ended questions that are not analyzed in the present paper. Because most variables were non-normally distributed, non-parametric statistical methods were applied, including Spearman’s rank correlations, Mann–Whitney U tests, and Kruskal–Wallis tests. Perceived learning usefulness was strongly and positively associated with both frequency of AI use and satisfaction with the learning process. Ethical attitudes were also positive, but more weakly associated with frequency of use. Demographic group differences were observed mainly in specific usage patterns rather than in general attitudes towards AI-supported learning. These exploratory findings suggest that perceived learning usefulness remains relevant in mandatory AI-integration contexts. Pedagogical scaffolding—including prompt literacy, verification practices, and reflective documentation—provides a structured framework for guided and responsible use of generative AI tools in higher education.
Emese Belényesi, M. Korpics, Tamás Méhes et al.· Trends in Higher Education· 0 citations
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